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Restore Trust in Your Data Using a Business-Aligned Data Quality Management Approach
Great data-driven insights start with great data quality.
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Contributors
- Akin Akinwumi, Startech.com
- Patrick Bossey, Crawford and Co.
- Diraj Goel, Reach Social Inc.
- Anonymous contributors
Your Challenge
- With the ever-increasing demand from the business to make data-driven decisions, IT is struggling to ensure quality in the large volumes and variety of data.
- IT consistently hears that the reports that the business relies on are not accurate, and they are required to perform time-intensive and costly manual fixes to the organization’s data.
- Data users do not trust the data quality in your company. Business users bypass IT and establish their own data silos to manage quality. Without governance or consistency, data quality issues are propagated further within data silos.
- Data is not usable or useful to the business due to deficiencies in quality and processes, resulting in decisions being made based on intuition and weak analysis. Worse, these decisions can be based on the wrong data.
- With sub-optimal data quality, companies are unable to launch related data initiatives such as business intelligence, master data management, and big data. They need a clear understanding of business requirements and the implementation of data quality processes first.
Our Advice
Critical Insight
- Data quality means tolerance, not perfection. One-hundred percent pure quality data does not exist. Instead, think of data quality as a level of error that is tolerated by the business. Tolerance for data quality is unique to each business unit, and “good enough” for the business must be determined before embarking on costly and lengthy data quality repairs. If the data allows the business to operate at the desired level, don’t waste time fixing data that may not need to be fixed.
- Prevention is 10x cheaper than remediation. While going through this blueprint, you will learn how to address data quality issues at the root. Stop fixing data quality with band-aid solutions and start fixing it by healing it at the source of the problem. This will prevent costly and lengthy data quality repairs that require manual repairs. Address and resolve the problem at the point of ingestion and at process level to ensure going forward the data quality is up to standard.
- Data quality without data governance is equivalent to treating the symptoms but not curing the disease. To ensure that data quality fixes address the root causes of the problems, as well as to maintain that mindset and effort throughout the organization, data quality processes must have the appropriate oversight and governance. If data quality is not embedded into the enterprise data governance framework, data quality management will remain a band-aid fix.
Impact and Result
- Implement a set of data quality initiatives that are aligned with overall business objectives and aimed at addressing data practices and the data itself.
- Develop a prioritized data quality improvement project roadmap and long-term improvement strategy.
- Build related practices such as business intelligence and analytics with more confidence and less risk after achieving an appropriate level of data quality.
Start here – read the Executive Brief
Read our concise Executive Brief to find out why your organization should be relying on its data to make decisions, and why that data needs to be of high quality to support downstream data-driven decision making.
1. Define your organization’s data quality practice
Learn about what causes data quality issues, how to measure data quality health, and what makes a good data quality practice – and create a plan to improve it.
2. Analyze your priorities for data quality fixes
Determine your business unit priorities to create data quality improvement projects.
3. Fix your data quality issues
Revisit the root causes of data quality issues and identify the relevant root causes to the highest priority business unit, then determine a strategy for fixing those issues.
4. Sustain your data quality practices
Identify strategies for continuously monitoring and improving data quality at the organization.
Guided Implementations
This guided implementation is a nine call advisory process.
Guided Implementation #1 - Define your organization’s data quality practice
Call #1 - Learn about the concepts of data quality and the common root causes of poor data quality.
Call #2 - Identify the core capabilities of IT for improving data quality on an enterprise scale.
Call #3 - Create a strategy for improving these capabilities.
Guided Implementation #2 - Analyze your priorities for data quality fixes
Call #1 - Determine which business units use data and require data quality remediation.
Call #2 - Create a plan for addressing business unit data quality issues according to priority of the business units based on value and impact of data.
Guided Implementation #3 - Fix your data quality issues
Call #1 - Revisit the root causes of data quality issues and identify the relevant root causes to the highest priority business unit.
Call #2 - Determine a strategy for fixing data quality issues for the highest priority business unit.
Guided Implementation #4 - Sustain your data quality practices
Call #1 - Identify strategies for continuously monitoring and improving data quality at the organization.
Call #2 - Learn how to incorporate data quality practices in the organization’s larger data management and data governance frameworks.
Info-Tech Academy
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Data Quality Course
A manifesto for strategic data quality improvement.
This course makes up part of the Data & BI Certificate.
Course information:
- Title: Data Quality Course
- Number of Course Modules: 5
- Estimated Time to Complete: 2-2.5 hours
- Featured Analysts:
- Crystal Singh, Research Director, Applications
- David Piazza, VP of Research & Advisory, Applications Practice
- Now Playing: Academy: Data Quality | Executive Brief
Book Your Workshop
Onsite workshops offer an easy way to accelerate your project. If you are unable to do the project yourself, and a Guided Implementation isn't enough, we offer low-cost onsite delivery of our project workshops. We take you through every phase of your project and ensure that you have a roadmap in place to complete your project successfully.
Module 1: Define and Assess Your Organization’s Data Quality Practice
The Purpose
- Evaluate the maturity of the existing data quality practice and activities.
- Assess how data quality is embedded into related data management practices.
- Envision a target state for the data quality practice.
Key Benefits Achieved
- Understanding of the current data quality landscape.
- Gaps, inefficiencies, and opportunities in the data quality practice are identified.
- Target state for the data quality practice is defined.
Activities
Outputs
Explain approach and value proposition.
- Data Quality Management Primer
Detail business vision, objectives, and drivers.
- Data Quality Organizational Business Context
Discuss data quality barriers, needs, and principles.
- Data Quality Heath Check
Assess current enterprise-wide data quality capabilities.
Identify data quality practice future state.
Analyze gaps in data quality practice.
Module 2: Create a Strategy for Data Quality Project 1
The Purpose
- Define improvement initiatives.
- Define a data quality improvement strategy and roadmap.
Key Benefits Achieved
- Improvement initiatives are defined.
- Improvement initiatives are evaluated and prioritized to develop an improvement strategy.
- A roadmap is defined to depict when and how to tackle the improvement initiatives.
Activities
Outputs
Create business unit prioritization roadmap.
- Business Unit Prioritization Roadmap
Develop subject area project scope.
- Subject area scope
Subject area 1: data lineage analysis, root cause analysis, impact assessment, business analysis.
- Data Lineage Diagram
Module 3: Create a Strategy for Data Quality Project 2
The Purpose
- Define improvement initiatives.
- Define a data quality improvement strategy and roadmap.
Key Benefits Achieved
- Improvement initiatives are defined.
- Improvement initiatives are evaluated and prioritized to develop an improvement strategy.
- A roadmap is defined to depict when and how to tackle the improvement initiatives.
Activities
Outputs
Create business unit prioritization roadmap.
- Business Unit Prioritization Roadmap
Develop subject area project scope.
- Subject area scope
Subject area 1: data lineage analysis, root cause analysis, impact assessment, business analysis.
- Data Lineage Diagram
Module 4: Create a Plan for Sustaining Data Quality
The Purpose
- Plan for continuous improvement in data quality.
- Incorporate data quality management into the organization’s existing data management and governance programs.
Key Benefits Achieved
- Sustained and communicated data quality program.
Activities
Outputs
Formulate metrics for continuous tracking of data quality and monitoring the success of the data quality improvement initiative.
- Data Quality Practice Improvement Roadmap
Workshop debrief with project sponsor.
- Data Quality Improvement Plan (for defined subject areas)
Meet with project sponsor/manager to discuss results and action items.
Wrap up outstanding items from the workshop, deliverables expectations, guided implementations.
